Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event.

The quality of parent–child interaction is critical for child cognitive development. The Dyadic Parent–Child Interaction Coding System (DPICS) is commonly used to assess parent and child behaviors. However, manual annotation of DPICS codes by parent–child interaction therapists is a time-consuming task. To assist therapists in the coding task, researchers have begun to explore the use of artificial intelligence in natural language processing to classify DPICS codes automatically. In this study, we utilized datasets from the DPICS book manual, five families, and an open-source PCIT dataset. To train DPICS code classifiers, we employed the pre-trained fine-tuned model RoBERTa as our learning algorithm. Our study shows that fine-tuning the pre-trained RoBERTa model achieves the highest results compared to other methods in sentence-based DPICS code classification assignments. For the DPICS manual dataset, the overall accuracy was 72.3% (72.2% macro-precision, 70.5% macro-recall, and 69.6% macro-F-score). Meanwhile, for the PCIT dataset, the overall accuracy was 79.8% (80.4% macro-precision, 79.7% macro-recall, and 79.8% macro-F-score), surpassing the previous highest results of 78.3% accuracy (79% precision, 77% recall) averaged over the eight DPICS classes. These results show that fine-tuning the pre-trained RoBERTa model could provide valuable assistance to experts in the labeling process.

Speaker clustering is the task of identifying the unique speakers in a set of audio recordings (each belonging to exactly one speaker) without knowing who and how many speakers are present in the entire data, which is essential for speaker diarization processes. Recently, off-the-shelf deep speaker embedding models have been leveraged to capture speaker characteristics. However, speeches containing emotional expressions pose significant challenges, often affecting the accuracy of speaker embeddings and leading to a decline in speaker clustering performance. To tackle this problem, we propose DTG-VAE, a novel disentanglement method that enhances clustering within a Variational Autoencoder (VAE) framework. This study reveals a direct link between emotional states and the effectiveness of deep speaker embeddings. As demonstrated in our experiments, DTG-VAE extracts more robust speaker embeddings and significantly enhances speaker clustering performance.

Multimodal large language model (MLLM) agents connect perceptual inputs to tools and external actions, making safety a property of trajectories and retained state. This curated survey uses a constrained partially observable Markov decision process to organize six risk families: grounding failure, observation manipulation, miscalibrated authority, inference-time hijacking, unsafe commitment, and reusable-state corruption. We compare safeguards by mechanism, access requirements, and evidence scope, separating direct multimodal execution from transferred model-level and text-agent findings. The synthesis emphasizes image–text, GUI/web, and selected embodied settings; it is neither exhaustive nor independently coded. We distinguish response scores from executed violations and later-task reactivation, and propose containment and restoration outcomes for recovery evaluation. These distinctions motivate execution safeguards, calibrated autonomy, and integrity checks for reusable agent state.